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3D Shape Generation with Grid-based Implicit Functions
To remedy these issues, we propose to train the GAN on grids (i. e. each cell covers a part of a shape).
A Convolutional Decoder for Point Clouds using Adaptive Instance Normalization
In addition, we show that careful sampling is important both for the input geometry and in our point cloud generation process to improve results.
A Simple Approach to Intrinsic Correspondence Learning on Unstructured 3D Meshes
The question of representation of 3D geometry is of vital importance when it comes to leveraging the recent advances in the field of machine learning for geometry processing tasks.
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